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    Federated learning: Overview, strategies, applications, tools and future directions
    (Elsevier Ltd, 2024) Yurdem, Betul; Kuzlu, Murat; Gullu, Mehmet Kemal; Catak, Ferhat Ozgur; Tabassum, Maliha
    Federated learning (FL) is a distributed machine learning process, which allows multiple nodes to work together to train a shared model without exchanging raw data. It offers several key advantages, such as data privacy, security, efficiency, and scalability, by keeping data local and only exchanging model updates through the communication network. This review paper provides a comprehensive overview of federated learning, including its principles, strategies, applications, and tools along with opportunities, challenges, and future research directions. The findings of this paper emphasize that federated learning strategies can significantly help overcome privacy and confidentiality concerns, particularly for high-risk applications. © 2024 The Author(s)
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    Intelligent Diagnosis and Treatment Systems
    (CRC Press, 2024) Oksuz, Cosku; Yurdem, Betul; Gullu, Mehmet Kemal
    After the first unknown case of pneumonia emerged in China in December 2019, cases reported worldwide soon increased. The new type of coronavirus called SARS-CoV-2, which was determined to be the source of unknown pneumonia, caused the situation to be declared a pandemic within four months. After the past two years, the pandemic continued with the new mutations of the virus. The protracted pandemic has drastically impacted the whole world in many ways. The RT-PCR, which is accepted as the standard testing, has been used for detecting and isolating patients. Especially the high rates of false negatives for the RT-PCR test caused the need to develop alternative tools that are extremely sensitive. Therefore, many methods have been developed adopting machine- and deep-learning-based methods for recognizing COVID-19 disease over medical images. Many of these proposed intelligent systems are based on image-processing methods. More specifically, the researchers are rivaling in a manner to design deep learning based image-processing architectures for capturing the disease patterns effectively. In the scope of this study, the intelligent diagnosis methods proposed in the literature specifically for COVID-19 detection are overviewed by giving the logic behind and conceptualizing them. © 2025 Mustafa Berktas, Abdulkadir Hiziroglu, Ahmet Emin Erbaycu, Orhan Er and Sezer Bozkus Kahyaoglu.